HARLEY: A Semi-Automated detection of foci in fluorescence images of yeast

Quantification of cellular structures in fluorescence microscopy data is a key means of understanding cellular function. Unfortunately, numerous cellular structures present unique challenges in their ability to be unbiasedly and accurately detected and quantified. In our studies on stress granules in yeast, users displayed a striking variation of up to 3.7-fold in foci calls and were only able to replicate their results with 62-78% correlation, when requantifying the same images. To facilitate consistent results we developed HARLEY (Human Augmented Recognition of LLPS Ensembles in Yeast), a customizable software for detection and quantification of stress granules in S.cerevisiae. After a brief model training on ~20 cells the detection of foci is fully automated and based on closed loops in intensity contours, constrained only by the a-priori known size of the features of interest. Since no shape is implied, this method is not limited to round features, as is often the case with other algorithms. Candidate features are annotated with a set of geometrical and intensity-based properties to train a kernel Support Vector Machine to recognize features of interest. The trained classifier is then used to create consistent results across datasets. HARLEY is aimed at users without technical expertise, allows for batch processing and is freely available, which should be of broad interest to users focused on analysis of microscopy data in yeast.

Ekstraksi Relasi Antar Entitas di Bahasa Indonesia Menggunakan Neural Network

Dengan perkembangan zaman yang begitu pesat, berdampak pada perkembangan data pula. Salah satu bentuk data yang paling banyak saat ini berupa data tekstual seperti artikel sederhana maupun dokumen lain yang terdapat di internet. Agar data tekstual tersebut dapat dimengerti dan dimanfaatkan dengan baik oleh manusia, maka perlu di proses dan disederhanakan agar menjadi informasi yang ringkas dan jelas. Oleh karena itu, semakin berkembang pula penelitian dalam bidang Information Extraction (IE) dan salah satu contoh penelitian di IE adalah Relation Extraction (RE). Penelitian RE sudah banyak dilakukan terutama pada Bahasa Inggris dimana resourcenya sudah termasuk banyak. Metode yang digunakan pun bermacam-macam seperti kernel, tree kernel, support vector machine, long short-term memory, convulution recurrent neural network, dan lain sebagainya. Pada penelitian kali ini adalah penelitian RE pada Bahasa Indonesia dengan menggunakan metode convulution recurrent neural network yang sudah dipergunakan untuk RE Bahasa Inggris. Dataset yang digunakan pada penelitian ini adalah dataset Bahasa Indonesia yang berasal dari file xml wikipedia. File xml wikipedia ini kemudian diproses sehingga menghasilkan dataset seperti yang digunakan pada CRNN dalam Bahasa inggris yaitu dalam format SemEval-2 Task 8. Uji coba dilakukan dengan berbagai macam perbandingan data training dan testing yaitu 80:20, 70:30, dan 60:40. Selain itu, parameter pooling untuk CRNN yang digunakan ada dua macam yaitu ‘att’ dan ‘max’. Dari uji coba yang dilakukan, hasil yang didapatkan adalah bervariasi mulai dari mendekati maupun lebih baik bila dibandingkan dengan CRNN dengan menggunakan dataset Bahasa inggris sehingga dapat disimpulkan bahwa dengan CRNN ini bisa digunakan untuk proses RE pada Bahasa Indonesia apabila dataset yang digunakan sesuai dengan penelitian sebelumnya.

PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

Eye Movement Feature Set and Predictive Model for Dyslexia

Dyslexia is a learning disorder that can cause difficulties in reading or writing. Dyslexia is not a visual problem but many dyslexics have impaired magnocellular system which causes poor eye control. Eye-trackers are used to track eye movements. This research work proposes a set of significant eye movement features that are used to build a predictive model for dyslexia. Fixation and saccade eye events are detected using the dispersion-threshold and velocity-threshold algorithms. Various machine learning models are experimented. Validation is done on 185 subjects using 10-fold cross-validation. Velocity based features gave high accuracy compared to statistical and dispersion features. Highest accuracy of 96% was achieved using the Hybrid Kernel Support Vector Machine- Particle Swarm Optimization model followed by the Xtreme Gradient Boosting model with an accuracy of 95%. The best set of features are the first fixation start time, average fixation saccade duration, the total number of fixations, total number of saccades and ratio between saccades and fixations.

Eye Movement Feature Set and Predictive Model for Dyslexia

Dyslexia is a learning disorder that can cause difficulties in reading or writing. Dyslexia is not a visual problem but many dyslexics have impaired magnocellular system which causes poor eye control. Eye-trackers are used to track eye movements. This research work proposes a set of significant eye movement features that are used to build a predictive model for dyslexia. Fixation and saccade eye events are detected using the dispersion-threshold and velocity-threshold algorithms. Various machine learning models are experimented. Validation is done on 185 subjects using 10-fold cross-validation. Velocity based features gave high accuracy compared to statistical and dispersion features. Highest accuracy of 96% was achieved using the Hybrid Kernel Support Vector Machine- Particle Swarm Optimization model followed by the Xtreme Gradient Boosting model with an accuracy of 95%. The best set of features are the first fixation start time, average fixation saccade duration, the total number of fixations, total number of saccades and ratio between saccades and fixations.

Anomaly Detection of Electric Gate Valve Based on Multi-Kernel Support Vector Machine

Valve is an indispensable fluid control component in nuclear power system. Nuclear power station has a large number of gate valve equipment, which works under high temperature, high pressure, high radioactivity and other harsh conditions. In nuclear power plant accidents and economic losses, a considerable part of them are caused by valve failure. Aiming at the fault of electric gate valve, this paper proposes an anomaly detection method based on multi-kernel support vector machine. Firstly, the acoustic emission instrument is used to measure the fault state data and extract the fault features. Secondly, on the basis of classical support vector machine, multiple kernel function combinations are selected to decompose the model into convex optimization problems to realize the abnormal state detection of internal leakage fault of electric gate valve in nuclear power plant. The results show that, compared with the classical support vector machine method, the constructed support vector machine method based on multikernel learning has better effect and higher accuracy in anomaly detection of electric gate valve.